

from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser



@dataclass
class        A__:
						"""simple docstring"""






						_A    :				str       					=			field(
						    metadata={'''help''': '''The output directory where the model will be written.'''},  )
						_A    :				str       					=			field(
						    metadata={
						        '''help''': (
						            '''The encoder model checkpoint for weights initialization.'''
						            '''Don\'t set if you want to train an encoder model from scratch.'''
						        )
						    },  )
						_A    :				str       					=			field(
						    metadata={
						        '''help''': (
						            '''The decoder model checkpoint for weights initialization.'''
						            '''Don\'t set if you want to train a decoder model from scratch.'''
						        )
						    },  )
						_A    :				Optional[str]       					=			field(
						    default=a_,  metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''}							)
						_A    :				Optional[str]       					=			field(
						    default=a_,  metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''}							)







def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Any 							= HfArgumentParser((ModelArguments,))
			((a_)							,					)						:						List[str] 							= parser.parse_args_into_dataclasses()

			# Load pretrained model and tokenizer

			# Use explicit specified encoder config
			if model_args.encoder_config_name:
						a_						:						List[str] 							= AutoConfig.from_pretrained(model_args.encoder_config_name)
			# Use pretrained encoder model's config
			else:
						a_						:						List[str] 							= AutoConfig.from_pretrained(model_args.encoder_model_name_or_path)

			# Use explicit specified decoder config
			if model_args.decoder_config_name:
						a_						:						Optional[int] 							= AutoConfig.from_pretrained(model_args.decoder_config_name)
			# Use pretrained decoder model's config
			else:
						a_						:						Dict 							= AutoConfig.from_pretrained(model_args.decoder_model_name_or_path)

			# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
			a_						:						List[str] 							= True
			a_						:						str 							= True

			a_						:						Any 							= FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
			    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path  ,	decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path  ,	encoder_config=a__  ,	decoder_config=a__  ,	)

			# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
			a_						:						Dict 							= decoder_config.decoder_start_token_id
			a_						:						Any 							= decoder_config.pad_token_id
			if decoder_start_token_id is None:
						a_						:						Optional[Any] 							= decoder_config.bos_token_id
			if pad_token_id is None:
						a_						:						int 							= decoder_config.eos_token_id

			# This is necessary to make Flax's generate() work
			a_						:						Optional[int] 							= decoder_config.eos_token_id
			a_						:						int 							= decoder_start_token_id
			a_						:						Optional[int] 							= pad_token_id

			a_						:						Optional[Any] 							= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path)

			a_						:						List[Any] 							= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path)
			a_						:						int 							= tokenizer.convert_ids_to_tokens(model.config.pad_token_id)

			model.save_pretrained(model_args.output_dir)
			image_processor.save_pretrained(model_args.output_dir)
			tokenizer.save_pretrained(model_args.output_dir)


if __name__ == "__main__":
	main()





from __future__ import annotations


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						List[str] 							= 0
			a_						:						str 							= len(a__) - 1

			while i < j:
						if nums[i] + nums[j] == target:
									return [i, j]
						elif nums[i] + nums[j] < target:
									a_						:						List[Any] 							= i + 1
						else:
									a_						:						Optional[Any] 							= j - 1

			return []


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F"""{two_pointer([2, 7, 11, 15], 9) = }""")


